Job Description
At Syngenta, our goal is to build the most collaborative and trustworthy team in agriculture, providing top-quality seeds and innovative crop protection solutions that improve farmers' success. To support this mission, Syngenta’s IT & Digital Team is seeking a Computational Agronomy Scientist in Durham, NC. This role will lead complex and ambiguous agronomic initiatives from initial problem definition through implementation, adoption, and value realization.
In this senior individual-contributor role, you will:
- Solve complex problems across crop growth, physiology, disease, pest epidemiology, nutrition, abiotic stress, and seed placement.
- Partner with stakeholders to define the right problem, objective, scope, and success measures before work begins.
- Determine the scientific approach when an established method or solution does not exist.
- Lead multidisciplinary workstreams involving contributors across teams, disciplines, and geographic locations.
- Ensure the scientific integrity, reproducibility, implementation, and adoption of agronomic models and recommendations.
- Develop scientific and technical standards rather than simply applying existing practices.
- Represent Computational Agronomy in cross-functional, scientific, and external forums.
This is a Work Level 5B individual-contributor role. It may include day-to-day direction of interns and contractors but does not include direct line management of employees.
Accountabilities:
Scope and Accountability
- Own the scientific integrity, delivery, adoption, and value realization of assigned workstreams.
- Establish the scientific approach when no existing method adequately addresses the problem.
- Clearly document assumptions, uncertainty, limitations, and conditions under which a model or recommendation is valid.
- Develop and advance the domain’s scientific, analytical, modeling, and reproducibility standards.
- Build relationships with regional, product, platform, commercial, and scientific stakeholders.
- Provide onboarding, technical guidance, knowledge transfer, and evidence-based feedback for workstream contributors.
- Create documentation, processes, and capabilities that remain valuable beyond the individual project or scientist.
Problem Framing and Scientific Direction
- Partner with stakeholders to define the underlying agronomic problem before developing a solution.
- Challenge requests constructively when the proposed objective or method does not address the actual need.
- Establish the workstream’s objective, scope, success criteria, deliverables, and scientific boundaries.
- Determine the appropriate scientific approach and explain the alternatives considered.
- Define the model strategy, including calibration protocols, validation methods, performance criteria, and monitoring expectations.
- Identify data requirements, gaps, quality concerns, fitness limitations, and sources of uncertainty.
- Clearly communicate assumptions, risks, limitations, and the model’s approved domain of validity.
Workstream Ownership and Delivery
- Lead multidisciplinary workstreams spanning multiple projects, teams, geographic locations, and planning cycles.
- Manage the workstream from initial definition through development, implementation, adoption, and value realization.
- Identify, negotiate, and sequence dependencies involving teams that do not report directly to the role.
- Prioritize work based on scientific value, business impact, customer needs, resource constraints, and technical dependencies.
- Make trade-offs transparent and ensure contributors remain focused on agreed outcomes.
- Deliver workstreams according to established specifications, quality standards, and deadlines.
- Confirm that solutions are adopted, produce measurable value, and leave behind sustainable documentation and capability.
Scientific and Methodological Leadership
- Guide advanced experimental, analytical, statistical, and modeling approaches across studies and workstreams.
- Design or oversee multi-location field studies and evaluate the quality of their resulting data.
- Assess emerging scientific and computational methods based on evidence and practical agronomic value.
- Review models, analytical methods, documentation, and code developed by other contributors.
- Strengthen scientific, analytical, modeling, code-quality, and reproducibility standards across the team.
- Ensure workstream results are scientifically defensible, reproducible, and appropriately documented.
- Capture and share negative or inconclusive findings so the organization can learn from them.
Stakeholder Partnership and Representation
- Manage relationships with stakeholders across regional, product, platform, commercial, and scientific functions.
- Navigate conflicting priorities and recommend an appropriate path based on evidence and business value.
- Set realistic expectations and decline requests when scientific evidence does not support the proposed direction.
- Build alignment and influence technical, scientific, and business decisions without relying on formal authority.
- Translate complex science, uncertainty, and model limitations into decision-ready recommendations.
- Present workstream strategy, progress, outcomes, and risks to senior audiences.
- Represent Computational Agronomy in cross-functional initiatives, external partnerships, and scientific forums.
Coordination, Mentoring, and Capability Building
- Coordinate contributors across disciplines, teams, and locations while maintaining clear priorities and accountability.
- Define, sequence, review, and accept work completed by interns, contractors, and other workstream contributors.
- Provide effective onboarding, technical direction, coaching, and ongoing knowledge transfer.
- Give timely, specific, and evidence-based performance feedback to the appropriate hiring or people manager.
- Mentor scientists and technical contributors through scientific guidance, model review, code review, and constructive feedback.
- Build team capability by sharing reusable methods, standards, documentation, and lessons learned.
- Support a collaborative environment in which contributors can challenge assumptions and continuously improve their work.
Innovation and AI Adoption
- Identify emerging scientific, statistical, computational, and agronomic methods relevant to the organization.
- Evaluate new methods based on scientific evidence, scalability, business value, and practical applicability.
- Convert promising research and technical approaches into repeatable working practices.
- Use generative AI and AI-assisted coding to accelerate research, analysis, documentation, and development.
- Demonstrate effective AI applications and help other contributors build confidence and fluency.
- Establish appropriate quality controls for AI-assisted scientific and technical work.
- Contribute expertise to departmental initiatives beyond the immediate workstream.